With the advent of always-on ubiquitous wireless connectivity, people are continuously generating ever-increasing amounts of personal data. For example, devices such as smartphones, smart watches, and wireless sensors collect data such as users' location history. Internet browsing history, conversations with digital assistants, books browsed and read, vital statistics as monitored by fitness bands, etc. As various data streams from such devices proliferate, it becomes increasingly challenging to mine the data effectively to generate personal insights about users, and to utilize those insights to serve users in more customized and relevant ways.
It would be desirable to provide novel and effective techniques for extracting actionable insights about users from various data streams, and to design a personal digital assistant that utilizes the insights to generate customized, relevant recommendations for users.